The plots of Rt over time have started to show evidence of an increase, following a period of downward trend during Autumn. This results in most of the Rt values being around 1. Rt is certainly above 1 in the SE and EE, and almost certainly above 1 in the SW, London, and NW.
As a result, the number of infections has definitely increased with strong growth in the SE, EE and SW, moderate growth in London, a resurgence in the EM, NE and NW, and a potential plateauing in the WM and YH.
Incidence of deaths levelled off during the last week of November / first week of December, with some falls noted in the North East, North West, and Yorkshire & Humberside, but are expected to start to climb significantly throughout December and early January in all regions.
It is now possible to estimate that the latest lock-down has contributed to the continuation of the downwards trends in Rt and the slowing down in the growth in the number of infections. This contribution appears quite modest and now these trends have reverted in most regions, in particular in the SE, EE, London and SW. The impact of the increased restrictions announced on Saturday 19th December (Tier 4) cannot yet be measured, and therefore any impact from them are excluded from these results.
Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.
We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.
We use:
Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).
Value of \(R_t\), the average number of secondary infections due to a typical infection today.
## `summarise()` ungrouping output (override with `.groups` argument)
The percentage of a given group that has been infected.
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'region' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'region' (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
NB: negative growth rates are rates of decline. Values are daily changes.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | 0.02 | 0.02 | 0.03 |
| East of England | 0.05 | 0.03 | 0.06 |
| East Midlands | 0.01 | -0.01 | 0.03 |
| London | 0.02 | 0.00 | 0.04 |
| North East | 0.00 | -0.02 | 0.03 |
| North West | 0.01 | 0.00 | 0.03 |
| South East | 0.04 | 0.02 | 0.05 |
| South West | 0.03 | 0.01 | 0.05 |
| West Midlands | 0.00 | -0.02 | 0.01 |
| Yorkshire and The Humber | 0.00 | -0.02 | 0.01 |
Halving times in days, if a region shows growth than value will be NA.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | NA | NA | NA |
| East of England | NA | NA | NA |
| East Midlands | NA | 90.61 | NA |
| London | NA | NA | NA |
| North East | NA | 37.04 | NA |
| North West | NA | 238.78 | NA |
| South East | NA | NA | NA |
| South West | NA | NA | NA |
| West Midlands | 480.50 | 30.85 | NA |
| Yorkshire and The Humber | 263.58 | 31.20 | NA |
Doubling times in days, if a region shows decline then the value will be NA.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | 30.08 | 22.14 | 45.68 |
| East of England | 14.98 | 11.11 | 24.65 |
| East Midlands | 68.94 | 27.26 | NA |
| London | 36.23 | 19.57 | 2491.48 |
| North East | 155.30 | 27.65 | NA |
| North West | 49.14 | 23.61 | NA |
| South East | 18.11 | 13.34 | 30.11 |
| South West | 26.47 | 15.35 | 125.56 |
| West Midlands | NA | 47.35 | NA |
| Yorkshire and The Humber | NA | 48.24 | NA |
NB: negative growth rates are rates of decline. Values are daily changes.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | 0.03 | 0.02 | 0.04 |
| East of England | 0.06 | 0.05 | 0.08 |
| East Midlands | 0.02 | 0.00 | 0.04 |
| London | 0.04 | 0.02 | 0.06 |
| North East | 0.01 | -0.02 | 0.03 |
| North West | 0.02 | 0.00 | 0.04 |
| South East | 0.05 | 0.04 | 0.07 |
| South West | 0.03 | 0.01 | 0.05 |
| West Midlands | 0.02 | 0.00 | 0.03 |
| Yorkshire and The Humber | 0.00 | -0.02 | 0.02 |
Halving times in days, if a region shows growth than value will be NA.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | NA | NA | NA |
| East of England | NA | NA | NA |
| East Midlands | NA | NA | NA |
| London | NA | NA | NA |
| North East | NA | 36.94 | NA |
| North West | NA | 416.65 | NA |
| South East | NA | NA | NA |
| South West | NA | NA | NA |
| West Midlands | NA | NA | NA |
| Yorkshire and The Humber | NA | 38.18 | NA |
Doubling times in days, if a region shows decline then the value will be NA.
| Region | Median | 95% CrI (lower) | 95% CrI (upper) |
|---|---|---|---|
| England | 22.70 | 18.31 | 29.68 |
| East of England | 10.74 | 8.29 | 15.30 |
| East Midlands | 33.84 | 18.09 | 367.17 |
| London | 18.80 | 12.42 | 40.37 |
| North East | 118.11 | 22.41 | NA |
| North West | 39.07 | 18.99 | NA |
| South East | 13.23 | 10.16 | 19.73 |
| South West | 22.06 | 12.91 | 81.82 |
| West Midlands | 41.11 | 20.67 | 17009.41 |
| Yorkshire and The Humber | 352.40 | 33.89 | NA |
The blue lines is show when interventions have been introduced (lockdown on 23 Mar and the relaxation of measures on 11 May), and the red line shows the date these results were produced (18 Dec).
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
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## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.
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Copyright © MRC Biostatistics Unit, University of Cambridge